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Re: st: Methods for selection bias
If you are interested in selection bias, you cannot avoid reading Heckman:
Heckman, J. (1979) `Sample selection bias as a
specification error', Econometrica, vol. 47, 1.
Heckman, J. (1990) `Varieties of selection bias',
The American Economic Review, vol. 80, 2.
Carneiro, Hansen and Heckman (2003), `Estimating...', International
Economic Review, May, Vol. 44, No.2
Rafa
----- Original Message -----
From: "Anders Alexandersson" <[email protected]>
To: <[email protected]>
Sent: Friday, April 21, 2006 2:45 PM
Subject: Re: st: Methods for selection bias
I would consider -permute- for permutation tests based on Monte Carlo
simulations. What assumptions can you make, e.g., do you have
independent samples (i.e. unmatched data)?
Anders Alexandersson
[email protected]
Privately, Hema replied:
The "non-randomised data" refers to both the sample and the treatment
assignment.
On 4/20/06, Anders Alexandersson <[email protected]> wrote:
Does "non-randomised data" here refer to the sample and/or to the
treatment assignment?
On 4/20/06, Hema Mistry <[email protected]> wrote:
> I was wondering whether you can provide me with some advice or point me
> in the right
> direction. I am trying to find methods which can deal with data that
> is non-randomised
> and suffers from selection bias. After searching various databases etc
> I have come up
> with the following methods:
> 1) Regression analyses
> 2) Propensity score - matching, stratification, regression,
> classification trees
> 3) Instrumental variables
> 4) Sample selection models
> 5) Two-part models
> 6) Inverse probability weighting
>
> Before I start using these methods in various datasets I was just
> wondering whether
> users are aware of any other methods which I have not identified?
> Can you recommend any good text books or key people that maybe I should
> contact?
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